Instructions to use eyad-silx/progress with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use eyad-silx/progress with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf eyad-silx/progress # Run inference directly in the terminal: llama cli -hf eyad-silx/progress
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf eyad-silx/progress # Run inference directly in the terminal: llama cli -hf eyad-silx/progress
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf eyad-silx/progress # Run inference directly in the terminal: ./build/bin/llama cli -hf eyad-silx/progress
Use Docker
docker model run hf.co/eyad-silx/progress
- Llama new
- LM Studio
- Jan
- Ollama
How to use eyad-silx/progress with Ollama:
ollama run hf.co/eyad-silx/progress
- Unsloth Desktop
- Pi
How to use eyad-silx/progress with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf eyad-silx/progress
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "eyad-silx/progress" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use eyad-silx/progress with Docker Model Runner:
docker model run hf.co/eyad-silx/progress
- Lemonade
How to use eyad-silx/progress with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull eyad-silx/progress
Run and chat with the model
lemonade run user.progress-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use eyad-silx/progress with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf eyad-silx/progress
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default eyad-silx/progress
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use eyad-silx/progress with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf eyad-silx/progress
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "eyad-silx/progress" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
push artifacts/scripts/hf_push.py
Browse files
artifacts/scripts/hf_push.py
CHANGED
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@@ -31,7 +31,10 @@ Pushed by `hf_push.py` β checks hourly and uploads any new/changed artifact.
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- `king-ix/` β incumbent king GGUF (Ingot-IX-27B-Q2) + provenance files (baseline)
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- `chal-identity/`, `chal-v1/`, `chal-n2/`, `chal-norms/` β challenger GGUF iterations
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(scale-refit / QAT variants; same 27B Q2 architecture as the king)
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- `states/` β QAT state checkpoints (`t0..tN.safetensors`)
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- `artifacts/` β `HANDOFF.md`, build/train/eval scripts, run logs, sample draws,
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measurement results (`measure*.jsonl`, `pick*.jsonl`, `drawn.jsonl`)
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- `MANIFEST.md` β size + sha256 + upload time of every artifact (regenerated each push),
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@@ -91,6 +94,10 @@ def big_tracks():
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("/root/models/chal-n2/chal-norms.gguf", "chal-n2/chal-norms.gguf"),
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("/root/models/chal-norms/chal-norms.gguf", "chal-norms/chal-norms.gguf"),
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("/root/models/king-ix/Ingot-IX-27B-Q2.gguf", "king-ix/Ingot-IX-27B-Q2.gguf")]
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for p in sorted(glob.glob("/root/states/*.safetensors")):
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t.append((p, "states/" + os.path.basename(p)))
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return t
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- `king-ix/` β incumbent king GGUF (Ingot-IX-27B-Q2) + provenance files (baseline)
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- `chal-identity/`, `chal-v1/`, `chal-n2/`, `chal-norms/` β challenger GGUF iterations
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(scale-refit / QAT variants; same 27B Q2 architecture as the king)
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- `states/` β QAT state checkpoints (`t0..tN.safetensors`); the quota-blocked ones
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(t2βt9, t15) are stored as `states/missing_t2-t9_t15.tar.zst` (restore: `tar --zstd -xf`)
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- `chal-n2/chal-norms.gguf.zst` β zstd-19 archive of the chal-n2 GGUF, uploaded compressed
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to fit the free-plan private-storage cap (raw sha256 in MANIFEST; restore: `zstd -d`)
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- `artifacts/` β `HANDOFF.md`, build/train/eval scripts, run logs, sample draws,
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measurement results (`measure*.jsonl`, `pick*.jsonl`, `drawn.jsonl`)
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- `MANIFEST.md` β size + sha256 + upload time of every artifact (regenerated each push),
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("/root/models/chal-n2/chal-norms.gguf", "chal-n2/chal-norms.gguf"),
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("/root/models/chal-norms/chal-norms.gguf", "chal-norms/chal-norms.gguf"),
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("/root/models/king-ix/Ingot-IX-27B-Q2.gguf", "king-ix/Ingot-IX-27B-Q2.gguf")]
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# quota workaround: zstd archives of files the free-plan cap cannot hold raw
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# (raw sha256s stay in the MANIFEST Pending section; restore: zstd -d / tar --zstd -xf)
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t += [("/root/models/chal-n2/chal-norms.gguf.zst", "chal-n2/chal-norms.gguf.zst"),
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("/root/states_missing_t2-t9_t15.tar.zst", "states/missing_t2-t9_t15.tar.zst")]
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for p in sorted(glob.glob("/root/states/*.safetensors")):
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t.append((p, "states/" + os.path.basename(p)))
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return t
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